Uniaxial tension fatigue tests conducted on dumbbell‐shaped specimens made of vulcanized natural rubber are carried out. Using the fatigue test data, a back‐propagation neural network (BPNN) model for estimating the fatigue life of natural rubber specimens is established. An improved sine‐cosine algorithm (ISCA) is proposed to optimize the parameters of the BPNN model. The peak engineering strain, ambient temperature, and Shore hardness of natural rubber specimens are used as the input variables while the rubber fatigue life as the output variable of the BPNN model. The regression results and predicted life distribution of the established BPNN model are encouraging. For comparison, a genetic algorithm, a particle swarm algorithm, and a standard sine‐cosine algorithm are also used to obtain the BPNN model parameters, respectively. The results showed that the prediction accuracy and efficiency of ISCA are better than other three algorithms. In addition, the sensitivity analysis is introduced to quantify the relative influence of the model inputs on the output.
The fatigue life of natural rubber (NR) components under a constant‐amplitude load is dispersed. The dispersion can be reflected by establishing a probabilistic fatigue life prediction model (also called the P–S–N curve). However, the effect of strain ratio on fatigue life is typically ignored in conventional P–S–N curves. Aiming at improving the prediction accuracy of conventional models, a unified P–S–N curve for NR components is proposed in the present study, in which the strain ratio effect is considered in the calculations. Using the dumbbell‐shaped rubber specimen fatigue test data, a polynomial function is proposed to describe the correlation between the equivalent strain amplitude, strain amplitude, and strain ratio. Then a calculation model of equivalent strain amplitude is established. Furthermore, a unified P–S–N curve for NR is established with equivalent strain amplitude as the damage parameter. By comparing the measured and predicted fatigue life of NR under different reliabilities, the accuracy of the established unified P–S–N curve is verified.
PurposeThe purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.Design/methodology/approachThe generalized Stockwell transform (GST) and the singular value ratio spectrum (SVRS) methods are combined. A time-frequency distribution measurement criterion named the energy concentration measurement (ECM) is initially used to determine the parameter of the optimal GST method. Then, the optimal GST is applied to conduct a time-frequency transformation for a raw signal. Subsequently, the two-dimensional time-frequency matrix is obtained. Finally, the improved singular value decomposition (SVD) analysis is used to conduct a noise reduction of the time-frequency matrix. The SVRS is proposed to select the effective singular values. Furthermore, the time-domain feature of the impact signal is obtained by taking the inverse GST transform.FindingsThe simulated and experimental signals are used to verify the superiority of the proposed method over conventional methods. The obtained results show that the proposed method can effectively extract fault features of the rolling element bearing.Research limitations/implicationsThis paper mainly discusses the application of GST and SVRS methods to analyze the weak fault feature extraction problem. The next research direction is to explore the application of the Hilbert Huang transform (HHT) and variational modal decomposition (VMD) in the impact feature extraction of rolling bearing.Originality/valueIn the present study, a new SVRS method is proposed to select the number of effective singular values. This paper proposed an effective way to obtain the fault feature in monitoring of rotating machinery.
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